he increasing popularity of MCMC algorithms and the need to tackle problems of growing complexity have brought high demands on the efficiency of this class of sampling methods, which are often undeniably costly. The answer to such demands has produced both a higher level of sophistication in the design of MCMC algorithms and the introduction of alternative approaches. In particular, the sampling and stochastic approximation landscape has been noticeably enriched by the introduction of Sequential Monte Carlo (SMC) algorithms and (stochastic) Cubature Methods (CM), both of them most often practically implemented in conjunction with the by now renowned particle methods (and with MCMC as well).
The aim of this workshop was to expose the participants and interested faculty to the most recent developments in the field of statistical sampling and stochastic approximation; and to have a truly interdisciplinary event, with the objective of fostering interactions between scientists involved in theoretical developments of sampling methods and those that use such algorithms in applied research.
